We developed a novel GC/MS data processing and analysis platform, MET-COFEI(METabolite COmpound Feature Extraction and Identification). MET-COFEI detects and clusters chromatograph peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then identify the compound by searching the reconstructed compound’s pure spectrum against an user-specific mass spectrum library. MET-COFEI integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection. We have also developed a new alignment algorithm that uses the constructed compound-associated spectrum instead of individual peaks to align the same metabolite compound across samples, which is expected to result in a more confident and comprehensive compound-associated peak information for downstream analyses. MET-COFEI has been systematically tested on a series of GC/MS profiles of mixed standards at different concentrations as well as real untargeted GC/MS plant metabolomics data. MET-COFEI provides a very excellent performance in detected chromatographic peaks when compared with other open-source software. MET-COFEI support batch and parallel mode to run large scale GC-MS data set.

Additionally, we also developed a series LC-MS data processing tools: MET-COFEA and MET-XAlign, which, together with MET-COFEI can provide a more comprehensively biological insight for mass spectrometry based metabolomics data.